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Data Mining Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic, 2nd Edition
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Mehmed Kantardzic, "Data Mining: Concepts, Models, Methods, and Algorithms, 2 edition"
2011 | ISBN: 0470890452 | 552 pages | EPUB, PDF | 13,4 MB
This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies:
Bayesian Networks (BN) methodology often used for causality modeling
Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks
CART algorithm and Gini index in building decision trees
Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm
Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning
PageRank algorithm for mining and authority ranking of web pages
Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents
New sections on temporal, spatial, web, text, parallel, and distributed data mining
More emphasis on business, privacy, security, and legal aspects of data mining technology
This text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here.
This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here.
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